tfp.mcmc.UncalibratedLangevin

Runs one step of Uncalibrated Langevin discretized diffusion.

Inherits From: TransitionKernel

tfp.mcmc.UncalibratedLangevin(
    target_log_prob_fn,
    step_size,
    volatility_fn=None,
    parallel_iterations=10,
    compute_acceptance=True,
    experimental_shard_axis_names=None,
    name=None
)

The class generates a Langevin proposal using _euler_method function and also computes helper UncalibratedLangevinKernelResults for the next iteration.

Warning: this kernel will not result in a chain which converges to the target_log_prob. To get a convergent MCMC, use MetropolisAdjustedLangevinAlgorithm(...) or MetropolisHastings(UncalibratedLangevin(...)).

For more details on UncalibratedLangevin, see MetropolisAdjustedLangevinAlgorithm.

Args
target_log_prob_fn	Python callable which takes an argument like current_state (or *current_state if it's a list) and returns its (possibly unnormalized) log-density under the target distribution.
step_size	Tensor or Python list of Tensors representing the step size for the leapfrog integrator. Must broadcast with the shape of current_state. Larger step sizes lead to faster progress, but too-large step sizes make rejection exponentially more likely. When possible, it's often helpful to match per-variable step sizes to the standard deviations of the target distribution in each variable.
volatility_fn	Python callable which takes an argument like current_state (or *current_state if it's a list) and returns volatility value at current_state. Should return a Tensor or Python list of Tensors that must broadcast with the shape of current_state Defaults to the identity function.
parallel_iterations	the number of coordinates for which the gradients of the volatility matrix volatility_fn can be computed in parallel.
compute_acceptance	Python 'bool' indicating whether to compute the Metropolis log-acceptance ratio used to construct MetropolisAdjustedLangevinAlgorithm kernel.
experimental_shard_axis_names	A structure of string names indicating how members of the state are sharded.
name	Python str name prefixed to Ops created by this function. Default value: None (i.e., 'mala_kernel').

Raises
ValueError	if there isn't one step_size or a list with same length as current_state.
TypeError	if volatility_fn is not callable.

Attributes
compute_acceptance
experimental_shard_axis_names	The shard axis names for members of the state.
is_calibrated	Returns True if Markov chain converges to specified distribution. TransitionKernels which are "uncalibrated" are often calibrated by composing them with the tfp.mcmc.MetropolisHastings TransitionKernel.
name
parallel_iterations
parameters	Return dict of __init__ arguments and their values.
step_size
target_log_prob_fn
volatility_fn

Methods

bootstrap_results

View source

bootstrap_results(
    init_state
)

Creates initial previous_kernel_results using a supplied state.

copy

View source

copy(
    **override_parameter_kwargs
)

Non-destructively creates a deep copy of the kernel.

Args
**override_parameter_kwargs	Python String/value dictionary of initialization arguments to override with new values.

Returns
new_kernel	TransitionKernel object of same type as self, initialized with the union of self.parameters and override_parameter_kwargs, with any shared keys overridden by the value of override_parameter_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

experimental_with_shard_axes

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experimental_with_shard_axes(
    shard_axis_names
)

Returns a copy of the kernel with the provided shard axis names.

Args
shard_axis_names	a structure of strings indicating the shard axis names for each component of this kernel's state.

Returns
A copy of the current kernel with the shard axis information.

one_step

View source

one_step(
    current_state, previous_kernel_results, seed=None
)

Runs one iteration of MALA.

Args
current_state	Tensor or Python list of Tensors representing the current state(s) of the Markov chain(s). The first r dimensions index independent chains, r = tf.rank(target_log_prob_fn(*current_state)).
previous_kernel_results	collections.namedtuple containing Tensors representing values from previous calls to this function (or from the bootstrap_results function.)
seed	PRNG seed; see tfp.random.sanitize_seed for details.

Returns
next_state	Tensor or Python list of Tensors representing the state(s) of the Markov chain(s) after taking exactly one step. Has same type and shape as current_state.
kernel_results	collections.namedtuple of internal calculations used to advance the chain.

Raises
ValueError	if there isn't one step_size or a list with same length as current_state or diffusion_drift.